This invention relates to a method and apparatus for testing and classification of automotive and other batteries.
In the automotive industry and likewise in other technical areas, there is a need for improvements in systems for battery testing. We have provided an advance in the art in terms of the testing system disclosed in EP 0 762 135 A2 (case 12 reference P52759EP) which discloses a method and apparatus for testing automotive electronic control units and batteries utilising neural networks to effect waveform analysis on a digitised signal. Battery testing is by waveform analysis of the battery current during transient connection of a load. A network learning stage is employed together with a recognition test routine for characteristic waveforms. This approach is based upon software simulation of the waveform analysis which may be carried out visually by a person skilled in the art to distinguish between the current or voltage profiles of various categories of batteries.
References identified in searches made with respect to the subject matter of the present application consist of the following:
U.S. Pat. No. 4,204,153
U.S. Pat. No. 5,469,528
U.S. Pat. No. 5,537,327
WO 96/35522
WO 96/05508
GB 2285317A
GB 2278452A
None of these is any more pertinent than WO 96/35522 which discloses the use of a capacitor discharge pulse as a battery test step to enable identification of battery type by reference to voltage gradient using a voltmeter of oscilloscope or digital signal processing device whereby the battery type may be recognized prior to sorting discarded batteries for recycling purposes.
EPO 772056A discloses a system for checking the charge-discharge cycles of a rechargeable battery utilizing neural networks which are trainable by collecting data during discharge cycles of a battery. The battery is tested by collecting current battery data and using the neural networks for predicting from the current data a time interval by which the battery voltage will reach a predetermined critical threshold level.
U.S. Pat. No. 5,596,260 discloses a battery testing apparatus and method utilizing an algorithm to determine the charge of a battery. Initially, a battery state model is established comprised of a number of discrete charge states ranging from full charge to no charge. The probability of the actual battery""s charge for a particular charge state is specified by the battery state model for each of the charge states. Then a discharge curve specifying the battery""s voltage as a function of time is determined and calibrated. Then the battery""s voltage is periodically measured, and based on the measured voltage and the discharge curve, a voltage probability distribution over the range of battery states is computed and the battery discharge model is updated to produce a discharge model having smaller variance. The charge of the battery corresponding to the mean value of the battery discharge model is then displayed to the user.
In a first aspect of the present invention we are seeking to provide a further technical advance in battery test systems whereby the approach of simulating the visual analysis carried out by a technical person is replaced by an alternative approach in which the technical attributes of computer systems are employed in a manner which exploits their inherent advantages rather than by seeking to constrain them to simulate a human analytical approach.
Accordingly, we have sought to utilise the capacity of software systems to process and analyse data relating to multiple parameters, in a way which the human brain finds difficult. In accordance with this approach, we realised that the multi-layer perceptron neural networks of our above-identified EP ""135 A2 specification had limitations in terms of their requirement for the input of an approximation to the required analytical answer in any case.
In accordance with this new approach to battery analysis, we sought to provide a system in which multiple battery parameters, not all of them necessarily being electric parameters, such as thermal parameters, would be fed into the system and the system would be capable of recognising particular characteristics of the complex data fed in and thereby to effect an efficient classification step, perhaps on the basis of relatively limited data as compared with that required for waveform analysis.
An object of this aspect of the present invention is to provide a method and apparatus for classification of automotive and other batteries in accordance with one or more sensed parameters thereof, offering improvements in relation to one or more of the matters discussed above, or generally.
According to the invention there is provided a method of making battery test apparatus and there is also provided apparatus for classification of automotive and other batteries, as defined in the accompanying claims.
In an embodiment of the invention the system is provided with a neural network in the form of a self-organising network, namely a Kohonen network. In the embodiment, this software network has undergone (or is based upon software which has undergone) a controlled degree of training. That training is based on a data input comprising a representative sample of battery data. Such a representative sample of battery data comprises a plurality of battery parameters selected from the group including voltage, current, internal resistance, surface charge/capacitance, and thermal parameters. This controlled degree of self-training provides a basis for the classification step required from the self-organising network.
In the embodiment also, the method and apparatus provides test data generation means as part of the system and which is adapted to subject a given battery on test to test routines to generate test data for the classification means. This test data is related to the representative sample battery data on which the software network was trained. For example, the software network may be trained on data from a sequence of transient battery loads or transient battery charging routines, these being separated likewise by relatively transient intervals. Where such a routine has been included in the training data, then the test data generation means included in the battery test apparatus is arranged to generate corresponding data which leads to relatively efficient battery classification.
In connection with the use of multiple transient battery loads in the battery test routine, this approach has practical significance in relation to the capacitance aspects of battery construction, namely that the sequence of multiple transient loads is capable of discharging the surface charge of the battery which is present due to the parallel plate construction of the latter and which can otherwise provide misleading data arising from the (for example) 15 volts potential difference theron arising from alternator ripple.
The embodiments of the invention are software-intensive rather than hardware-intensive when compared with previously known battery testing systems.
In accordance with a second aspect of the invention, there is a need in the automotive and other industries for improved methods and apparatus for the testing and classification of batteries, notably the provision of systems which permit the in-situ testing of batteries using a minimum of hardware and making low power requirements while offering hand-held operational characteristics and relatively rapid test/classification results, and an object of this aspect of the present invention is to provide a method and apparatus offering improvements in relation to one or more of these requirements, or indeed generally.
According to the invention there is provided a method and apparatus for the testing and/or classification of automotive and other batteries as defined in the accompanying claims.
In an embodiment of the invention described below there is provided a method and apparatus in which a battery is subjected to a microcycle test procedure and the result of that procedure is subjected to analysis by a neural network or by an adaptive algorithm, and by means of this new approach to the testing of automotive and other batteries, we have established that it is possible to test and classify batteries in accordance with their life and other criteria in a manner which yields advantages in accordance with several of the factors discussed above.
In the embodiment, the data analysis step is performed by means of an adaptive algorithm or by a neural network, and the battery test procedure includes the application of transient stimuli as part of a microcycle. The hardware provided for this purpose is minimised by this design approach and the test connection to the battery allows testing in-situ without violating automotive radio code systems nor electronic control unit software and the like.
The battery test routine, including the microcycle, provides for the application of a series of transient stimuli to the battery in the manner shown in the accompanying drawings, for example:
Voltage test;
Microload;
Surface charge removal;
Microload;
Recovery;
Microload;
Microcharge.
Within a microcycle of the above kind there may be a series of positive or negative pulses applied to the battery, such being implemented by a field effect transistor (FET) gate.
By the use of a microcycle of transient charges and/or loads applied to a battery in sequence in accordance with this aspect of the invention there is provided the advantage (with respect to our above-mentioned EP ""135A specification) that the practicality and cost-effectiveness of the system, particularly for use in a hand-held application, is unexpectedly advanced to an extent such that a relatively sophisticated analysis of a battery into the relevant one of a multiplicity of categories by use of an easily-operated hand-held item of equipment becomes a realistic option to an extent which is no hitherto the case.
The number of pulses provided in a microcycle is greater than one and generally anywhere in the range from 2 pulses to 100 or more. The voltages in the sequential pulses may be increasing or decreasing or equal. The number and frequency and voltage levels of the pulses may be determined by an initial voltage test applied to the battery, and/or ambient temperature and/or battery size, and then the pulse characteristics may be determined by data values obtained from subsequent pulses. In other words, the algorithm determining pulse numbers, frequency and levels is adaptive. Alternatively, prior knowledge relating to the battery can be used, at least in part, to set certain parameters of the pulse characteristics, these depending on such data as battery history, known behaviour etc.
In the embodiments of the invention in which the method and apparatus provides for battery testing and/or classification by use of a microcycle comprising two or more transient charges and/or loads applied to a battery in sequence, the use of such a testing routine is, to the best of the Applicant""s knowledge, novel in relation to test procedures for automotive and like or other battery systems. Conventional test procedures have hitherto involved procedures which may be termed microcycles which necessarily (and often by intention) involve the use of substantial charge flows and associated energy release, with corresponding associated technical shortcomings including provision of heat sink or similar means, and utility limitations restricting testing to non in-situ battery evaluation.
The described embodiments employ an analysis step or stage for battery data generated from the microcycle, and the embodiments employ a neural network or an adaptive algorithm to enable classification or numerical evaluation of a battery. The use of a neural network leads to the data processing advantages setforth in our prior application of Jun. 19, 1997.
In the embodiments, FIGS. 11 and 12 of the drawings show battery responses to microload and microcharge cycles for both xe2x80x9cgoodxe2x80x9d and xe2x80x9cbadxe2x80x9d batteries.
Data employed in the analysis step of the invention is employed directly from the microcycle routine. Data may be in wave form format or in discreet datapoints format, and may represent relative or absolute values of the sampled battery characteristics. These latter may include impedance, bounce-back, peaks, areas, charge take-up, start and end voltages etc.
In accordance with a third aspect of the present invention there is provided a system in which the design approach of the first and second aspects of the invention is modified in order to achieve a substantial simplification in terms of hardware/software provision and analytical procedures, while still being based upon the same analytical principle, and thus being able to provide a comparable result in terms of battery classification into the principal classes. In simple terms this aspect of the invention provides a method and apparatus which is capable of achieving an accurate battery classification into, for example, the classes of bad/good but discharged/good classes, but with the above-mentioned structural and procedural simplification, with the attendant corresponding cost advantages.
In accordance with this aspect of the invention, we have discovered that in the method and apparatus of the preceding aspect of the invention there can be achieved the above-identified simplifications by the elimination of the use of neural networks for analysis of the profiles of the battery parameters for classification purposes.
This simplification and corresponding advance was a relatively unexpected development in the course of our research on this project, despite the background experience which our earlier work involving the use of neural networks provided. However, it was precisely the knowledge of the results of test procedures involving neural networks which provided the data whereby simplified methods and apparatus could be adopted.
More specifically, knowledge of the results of test procedures with neural networks enabled us to identify the fact that a satisfactory correlation could be achieved between battery classifications and test procedure profiles using a relatively simple algorithmic procedure. This procedure utilises previously-established data relating to microcharge and/or microload voltage profiles in order to define time intervals (from a defined time such as the microload or microcharge commencement time) at which to take characteristic sample voltages which thereby become substantially as informative as analysis of the entire profile itself.
Accordingly, according to this third aspect of the present invention there is provided a method and apparatus as defined in the identified ones of the accompanying claims.
In the embodiment described below, the battery""s reaction to a transient test parameter applied to it provides a measure of the condition of the battery. By analysis of the waveform shape or profile (by reference to the corresponding voltage at a characteristic point) representative of the battery""s reaction to the test parameter, the corresponding classification step can be effected. This analysis step is effected in the embodiment without employing a neural network by means of an algorithm adapted to identify a corresponding one of two or more battery condition classifications, and the corresponding one of these classifications exhibits a correlation with (voltage aspects of) the waveform profile of the electrical value.
In the embodiment, voltage aspects of the waveform profile utilised for this purpose include voltage values giving a measure of impedance, voltage bounce-back, voltage peaks, voltage troughs, voltage shape or profile, charge take-up, and start and end voltages.
Also in the embodiment, the transient period of the microload or microcharge applied to the battery lies in the range of 1 to 1,000 milliseconds, and the repetition interval between successive microloads and/or microcharges varies from 2 to 100 times the duration of application of the microload or microcharge.
In this aspect of the invention, the ability to eliminate the neural network with its attendant cost despite the retention of an analysis basis (relating to test parameter profile) corresponding to that carried out by the neural network, is of significant value and benefit. The achievement of this step by a relatively simple algorithmic hardware/based routine enables a valuable product simplification to be achieved.